Long-term effects of autonomous vehicles on the built environment: a systematic scoping review towards conceptual frameworks

Figures & data

Figure 1. Systematic search process based on the PRISMA framework and search outcomes (Page et al., 2021).

Table 1. Indicators used to extract data for each built environment dimension.

Built Environment Dimension	Data Extraction Indicators
Parking	Demand for both off-street and on-street parking, measured by: 1. Number of parking spaces per vehicle/household. 2. Size of parking spaces per vehicle/household.
Destination Accessibility	Job and residential accessibilities represented by time, monetary and land use indicators.
Density	Population and number of jobs per unit of area.
Distance to CBD	Commute distance/time, average travel distance, urban area and household relocation.
Diversity of Land Uses	Land use entropy and parcel area disaggregated by type (i.e. commercial, residential, industrial, government, civic, education, etc).
Design	Changes in urban geometry and infrastructure, measured by: 1. Intersection density/block sizes. 2. Sizes of sidewalks, car lanes and cycle lanes. 3. Street network density (i.e. street area/total urban study area). 4. Pavement and utilities.

Table 2. Quantitative impacts of AVs on parking.

Parking
AV Type	Location	Impact	Description	Major Assumptions	Source
SAV	CBD	−90% to −70%	Parking demand reduction range.	SAV fleet size was 60,000 vehicles (i.e. 5.7% of the total number of private vehicles registered in 2018); each traveller performs only one trip during the morning trip hour, and no trip chains were assumed.	Kumakoshi et al. (2021)
SAV	CBD	−67% to −20%	Parking demand reduction range.	Year 2035; different SAV adoption rates (i.e. from 20% to 61%); both car-sharing and ride-sharing are considered as SAVs.	Simons et al. (2018)
SAV	Suburbs	−94%	Parking demand reduction.	SAV fleet size was 60,000 vehicles (i.e. 5.7% of the total number of private vehicles registered in 2018); each traveller performs only one trip during the morning trip hour, and no trip chains were assumed.	Kumakoshi et al. (2021)
SAV	Citywide	−90%	Reduction in parking space demand.	Study Area: UWE Frenchay campus. Scenario with 90% of the travel demand served by SAVs; all SAVs are singly occupied; one SAV is assumed to replace nine non-SAVs.	Okeke (2020)
SAV	Citywide	−4.5%	Parking demand reduction.	5% mode shift from human driven vehicles to SAVs; no induced demand is considered; the cost of SAV system is $0.5/min for single occupant, and $0.3/min for ride-sharing; maximum waiting time is 15 min.	Zhang and Guhathakurta (2017)
SAV	Citywide	−83%	Maximum potential reduction in parking.	90% of the travel demand served by SAVs; 50% with single occupancy and 40% ride-sharing; population and land use do not change;	Silva et al. (2021)
SAV	Citywide	−50%	Reduction in parking area.	SAV is used for home-work commuting; 100% of the travel demand served by SAVs; assumed maximum empty-cruising distance is 1.5 km.	Kondor et al. (2019)
SAV	Citywide	−96% to −88%	Parking demand reduction range.	10 × 10mi grid based hypothetical city is used; SAV has a low penetration rate of 2%; peak hour speed is 30mph, and off-peak speed is 21 mph.	Zhang et al. (2015)
PAV	Citywide	−32% to −15%	Parking area reduction range in four different scenarios.	Parking fees vary from $0 to $2/h.	Kang et al. (2022)
PAV	Citywide	7.4%	Parking demand change for single-user PAVs.	The commute cost is assumed as 50% of the fixed vehicle ownership cost.	Zakharenko (2016)
PAV + SAV	CBD	−75%	Parking area reduction.	Year 2040; Scenario with PAV (37.35%), SAV (37.35%) and human-driven vehicles (12.65% shared and 12.65% private).	Zhang and Wang (2020)
PAV + SAV	Suburbs	100%	Parking area increase.	Year 2040; Scenario with PAV (37.35%), SAV (37.35%) and human-driven vehicles (12.65% shared and 12.65% private).	Zhang and Wang (2020)
PAV + SAV	Citywide	−62% and −87%	Average and maximum car-park demand reduction.	All vehicles are the same size; car-park operator takes control of all AVs that enter the parking facility; the vehicles arrivals follow a Poisson distribution; only Avs park in this car-park.	Nourinejad et al. (2018)
PAV + SAV	Citywide	−50%	Car-park space reduction due to more efficient layout for AVs.	Vehicles can be controlled remotely; they park in same size cells; vehicle exit time is previously known, accurate positioning information.	Azevedo et al. (2020)
PAV + SAV	Citywide	−49% to −11%	Car-park area reduction range due to more efficient layout for AVs.	Single-story parking facility with no interior obstructions; all AVs are the same size; accurate positioning system; vehicles can be independently accessible at all times.	Kong et al. (2018)

Table 3. Quantitative impacts of AVs on residential and employment densities.

Residential Density
AV Type	Location	Impact	Description	Major Assumptions	Source
SAV	Suburbs	−3%	Change in population density in major suburban/rural areas in scenarios with large level-5 SAV penetration (PT-1).	Feedback effects of automation on road capacity, congestion and travel times are considered.	Gelauff et al. (2019)
SAV	Suburbs	0% to 0.4%	Population change range in outer suburban areas (zone D, E, F and G) due to the introduction of SAVs.	Virtual city model; the urban and transportation systems follow the economic equilibrium theory; lower value of time for AV riders since users can work and do leisure activities during the trip.	Hiramatsu (2022)
SAV	CBD	127%	Change in the population density.	40% of travel demand served by SAVs. Road capacity and value of time changes follow a meta-regression model including SAV penetration rate, road type and SAV automation level.	Zhong et al. (2020)
SAV	CBD	3%	Change in population density in four largest cities in scenario with large level-5 SAV penetration (PT-2).	Feedback effects of automation on road capacity, congestion and travel times are considered.	Gelauff et al. (2019)
SAV	CBD	0.8%	Increase in population increase in CBD area (zone A) due to the introduction of SAVs.	Virtual city model; the urban and transportation systems follow the economic equilibrium theory; lower value of time for AV riders since users can work and do leisure activities during the trip.	Hiramatsu (2022)
PAV	Suburbs	−0.5%	Change in the population density of suburbs outside the Randstad zone in scenario with large level-5 PAV penetration (CAR-2).	Feedback effects of automation on road capacity, congestion and travel times are considered.	Gelauff et al. (2019)
PAV	Suburbs	−1.3% to 0%	Population change range in scenarios 2 and 3 with PAV in the periphery (zone 3).	Different variations in road capacity (80% in interurban roads and 40% in urban roads) and induced demand (scenario 3 only). Induced demand is derived from the improvements in accessibility.	Cordera et al. (2021)
PAV	Suburbs	−19.3% to 2%	Population change range in outer suburban areas (zone D, E, F and G) due to the introduction of PAVs.	Virtual city model; the urban and transportation systems follow the economic equilibrium theory; lower value of time for AV riders since users can work and do leisure activities during the trip.	Hiramatsu (2022)
PAV	CBD	0%	Population change range in scenarios 2 and 3 with PAV in the CBD (zone 1).	Different variations in road capacity (80% in interurban roads and 40% in urban roads) and induced demand (scenario 3 only). Induced demand is derived from the improvements in accessibility.	Cordera et al. (2021)
PAV	CBD	−1%	Change in the population density in the four largest cities in scenario with large level-5 PAV penetration (CAR-2).	Feedback effects of automation on road capacity, congestion and travel times are considered.	Gelauff et al. (2019)
PAV + SAV	Suburbs	0%	Population change range in the periphery (zone 3).	Scenario 4 with variations in road capacity (80% in interurban roads and 40% in urban roads) and induced demand derived from accessibility changes.	Cordera et al. (2021)
PAV + SAV	Suburbs	−21.4% to 19.4%	Population change range in outer suburban areas (zone D, E, F and G) due to the introduction of PAVs and SAVs.	Virtual city model; the urban and transportation systems follow the economic equilibrium theory; lower value of time for AV riders since users can work and do leisure activities during the trip.	Hiramatsu (2022)
PAV + SAV	CBD	0%	Population change range in the CBD (zone 1).	Scenario 4 with variations in road capacity (80% in interurban roads and 40% in urban roads) and induced demand derived from accessibility changes.	Cordera et al. (2021)
PAV + SAV	CBD	34.3%	Increase in population in CBD area (zone A) due to the introduction of PAVs and SAVs.	Virtual city model; the urban and transportation systems follow the economic equilibrium theory; lower value of time for AV riders since users can work and do leisure activities during the trip.	Hiramatsu (2022)
Employment Density
SAV	CBD	104%	Job density increase in the city centre.	40% of travel demand served by SAVs. Road capacity and value of time changes follow a meta-regression model including SAV penetration rate, road type and SAV automation level.	Zhong et al. (2020)
PAV	Suburbs	−0.7% to −0.1%	Change range in the number of jobs in suburbs (zone 3).	Scenarios 2 and 3 with different variations in road capacity (80% in interurban roads and 40% in urban roads) and induced demand (scenario 3 only). Induced demand is derived from the improvements in accessibility.	Cordera et al. (2021)
PAV	CBD	0.2% to 1.6%	Change range in the number of jobs in the city centre (zone 1).	Scenarios 2 and 3 with different variations in road capacity (80% in interurban roads and 40% in urban roads) and induced demand (scenario 3 only). Induced demand is derived from the improvements in accessibility.	Cordera et al. (2021)
PAV + SAV	CBD	−0.8%	Change in the number of jobs in the city centre (zone 1).	Scenario 4 with increases in road capacity (80% in interurban roads and 40% in urban roads) and induced demand. Induced demand is derived from the improvements in accessibility.	Cordera et al. (2021)
PAV + SAV	Suburbs	0.6%	Change range in the number of jobs in suburbs (zone 3).	Major assumptions: scenario 4 with increases in road capacity (80% in interurban roads and 40% in urban roads) and induced demand. Induced demand is derived from the improvements in accessibility.	Cordera et al. (2021)

Table 4. Quantitative impacts of AVs on destination accessibility.

Destination Accessibility
AV Type	Location	Impact	Description	Major Assumptions	Source
SAV	CBD	−4% to 21%	Job accessibility change range in scenario with road congestion pricing for TAZs within the toll ring area.	40% SAV penetration rate; road capacity and value of time changes follow a meta-regression model including SAV penetration rate, road type and SAV automation level.	Zhong et al. (2020)
SAV	Suburbs	−31% to 33%	Job accessibility change range in scenario with road congestion pricing for TAZs outside the toll ring area.	40% SAV penetration rate; road capacity and value of time changes follow a meta-regression model including SAV penetration rate, road type and SAV automation level.	Zhong et al. (2020)
SAV	Citywide	−4.17 min	Change in average accessibility in daily average travel time savings.	Pricing is 75% of conventional taxis for single rides and 70% of single-ride servicès price for shared rides.	Zhou et al. (2021)
PAV	CBD	17.4% to 187.1%	Accessibility change range in the CBD (zone 1)	Scenarios 2 and 3 with different variations in road capacity (80% in interurban roads and 40% in urban roads) and induced demand (scenario 3 only). Induced demand is derived from the improvements in accessibility.	Cordera et al. (2021)
PAV	Suburbs	−1.2% to 37.6%	Accessibility change range in the periphery (zone 3).	Scenarios 2 and 3 with different variations in road capacity (80% in interurban roads and 40% in urban roads) and induced demand (scenario 3 only). Induced demand is derived from the improvements in accessibility.	Cordera et al. (2021)
PAV	Citywide	0% (1) and 10% (2)	Accessibility change averaged by population size in scenario 2. (1) conservative and (2) optmistic road capacity increases	Increase in road capacity from 80% (1) to 270% (2) for extra-urban areas, and 40% for urban roads; increase in travel demand by new users is 16%.	Meyer et al. (2017)
PAV + SAV	CBD	SG$ −0.7	Mediam accessibility change represented by consumer surplus in Singapore dollar.	SAV cost is 33% of current mobility on-demand services.	Nahmias-Biran et al. (2020)
PAV + SAV	CBD	−58.3% to −71.1%	Accessibility change range in the CBD (zone 3).	Scenario 4 with variations of road capacity (80% in interurban roads and 40% in urban roads) and induced demand. Induced demand is derived from the improvements in accessibility.	Cordera et al. (2021)
PAV + SAV	Suburbs	−73% to −77.8%	Accessibility change range in the periphery (zone 3).	Scenario 4 with increases in road capacity (80% in interurban roads and 40% in urban roads) and induced demand. Induced demand is derived from the improvements in accessibility.	Cordera et al. (2021)
PAV + SAV	Citywide	5.20 min	Change in average accessibility in daily time savings.	Pricing is 75% of conventional taxis for single rides and 70% of single-ride servicès price for shared rides.	Zhou et al. (2021)
PAV + SAV	Citywide	1.4% (1) and 10.3% (2)	Accessibility change averaged by population size in scenario 3. (1) conservative and (2) optmistic road capacity increases.	Increase in road capacity from 80% to 270% for extra-urban areas, and 40% for urban roads; increase in travel demand by new users is 16%. No ride-sharing.	Meyer et al. (2017)
PAV + SAV	Citywide	SG$ 1.08	Accessibility change range represented by consumer surplus in Singapore dollar.	SAV cost is 33% of current mobility on-demand services.	Nahmias-Biran et al. (2020)
PAV + SAV	Citywide	23.2% to 36.3%	Accessibility change range in four different scenarios considering different PAV and SAV penetration levels.	PAV ownership from 10% to 50%; SAV fleet size from 2% to 10% of total vehicle fleet; VoT from 10% to 60% of the current.	Luo et al. (2019)
PAV + SAV	Citywide	$6.6 to $9.20	Employment economic accessibility change range (i.e. consumer surplus variation) for four different classes of workers.	SAV travel cost is $0.30/mile; maximum SAV trip length is 10 miles for transit integration; no parking costs for SAVs; 5-minute pickup waiting time.	Ahmed et al. (2020)
PAV + SAV	Citywide	8.5% (1) and 8.9% (2)	Accessibility increases for low income (1) and high-income (2) residents.	Scenario with 30% increase in road capacity, −35% reduction in travel time perception and −50% parking costs	Childress et al. (2015)

Table 5. Quantitative impacts of AVs on distance to central business district.

Distance to CBD
AV Type	Location	Impact	Description	Major Assumptions	Source
SAV	Citywide	−3.6% to 13.8%.	Household median distances to CBD change range.	Scenarios with travel cost of $0.15/mile, different age groups and kids.	Zhang and Guhathakurta (2021)
SAV	Citywide	−4.2%	Change in the city radius (i.e. from 9.6–9.2 miles).	100% SAV adoption by commuters; virtual monocentric city model; economic equilibrium model is assumed; no parking is available.	Larson and Zhao (2020)
PAV	Citywide	14%	Change in the average commute distance.	The commute cost is assumed as 50% of the fixed vehicle ownership cost.	Zakharenko (2016)
PAV	Citywide	11.5%	Change in the city radius (i.e. from 9.6–10.7 miles).	100% PAV adoption by commuters; virtual monocentric city model; economic equilibrium model is assumed.	Larson and Zhao (2020)
PAV + SAV	Citywide	8% (3) −7% (5)	Changes in the average commute time to CBD.	Scenario 3 considers 40% lower value of time. Scenario 5 considers parking demand reduction and no VoT change.	Llorca et al. (2022)
PAV + SAV	Citywide	1.3%	Change in average commute distance to CBD.	Scenario with +100% road capacity, −20% VoT, and −100% intersection delay.	Nadafianshahamabadi et al. (2021)
PAV + SAV	Citywide	5.0%	Increase in the average trip distance.	Scenario 4 with PAVs and SAVs and increases in road capacity (80% in interurban roads and 40% in urban roads) and induced demand. Induced demand is derived from the improvements in accessibility.	Cordera et al. (2021)
PAV + SAV	Citywide	74% (1) 14%(2) 12%(3)	Home relocation reported in a stated-preference survey.	Scenario with AV level 4 technology available. (1) Stay at the same location. (2) Move closer to the CBD. (3) Move further from the CBD.	Bansal et al. (2016)
PAV + SAV	Citywide	82%(1) 7% (2) 11%(3)	Home relocation reported in a stated-preference survey.	Scenario with AV level 4 technology available. (1) Stay at the same location. (2) Move closer to the CBD. (3) Move further from the CBD.	Bansal and Kockelman (2018)
PAV + SAV	Citywide	30% (USA) 42% (China)	Home relocation reported in a stated-preference survey.	Percentage of respondents who reported willingness to move further from the city if AVs level-4 become available.	Guan et al. (2021)

Figure 2. Built Environment Impacts for Central Business Districts. SAVs, PAVs, SAVs + PAVs.

Figure 3. Built Environment Impacts for Suburbs. SAVs, PAVs, SAVs + PAVs.

Figure 4. Citywide Built Environment Impacts. SAVs, PAVs, SAVs + PAVs.

Table

Source	Method	Built Environment Indicator	Context	Source	Method	Built Environment Indicator	Context
Ahmed et al. (2020)	Modelling	Accessibility	USA	Liu et al. (2021)	Modelling	Commute time and residential density	General
Azevedo et al. (2020)	Modelling	Parking	General	Llorca et al. (2022)	Modelling	Household Relocation	Germany
Bahk et al. (2022)	Modelling	Parking	General	Luo et al. (2019)	Modelling	Accessibility and Household Relocation	Japan
Bansal and Kockelman (2018)	Survey and statistical analysis	Household Relocation	USA	Manivasakan et al. (2021)	Design Guidelines and Framework	Urban Design	Australia
Bansal et al. (2016)	Survey and statistical analysis	Household Relocation	USA	Meyer et al. (2017)	Modelling	Accessibility	Switzerland
Bin-Nun and Binamira, (2020)	Survey and Framework	Household Relocation	USA	Milakis et al. (2018)	Interviews with experts	Accessibility and Household Relocation	General
Bischoff et al. (2019)	Modelling	Parking	Germany	Milakis et al. (2017)	Literature Review	Several	General
Botello et al. (2019)	Interviews with experts	Urban Design	USA	Millard-Ball (2019)	Modelling	Parking	USA
Bridgelall and Stubbing (2021)	Modelling	Commercial Land Use	Canada	Moore et al. (2020)	Modelling	Household and Workplace Relocations	USA
Carrese et al. (2019)	Survey and modelling	Household Relocation	Italy	Nadafianshahamabadi et al. (2021)	Modelling	Road capacity and Land Use Relocation	USA
Cohen and Cavoli (2018)	Literature Review	Accessibility	General	Nahmias-Biran et al. (2020)	Modelling	Accessibility and Household Relocation	Singapore
Chapin et al. (2016)	Literature Review and Forecasting	Several	USA	Narayanan et al. (2020)	Literature Review	Several	General
Childress et al. (2015)	Modelling	Accessibility	USA	Nodjomian and Kockelman (2019)	Survey	Several	USA
Cordera et al. (2021)	Modelling	Accessibility and Land Use Relocation	Spain	Nogues et al. (2020)	Backcasting and interviews	Several	General
Dianin et al. (2021)	Literature Review and Framework	Accessibility and Land Use Relocation	General	Nourinejad et al. (2018)	Modelling	Parking	General
Duarte and Ratti (2018)	Literature Review	Several	General	Okeke (2020)	Modelling	Parking	England
Emberger and Pfaffenbichler (2020)	Modelling	Road capacity and Parking	Austria	Othman (2021)	Literature Review	Urban Design and Parking	General
Faisal et al. (2019)	Literature Review	Several	General	Overtoom et al. (2020)	Modelling	Parking and Road Capacity	Netherlands
Fagnant and Kockelman (2014)	Modelling	Road Capacity	USA	Rafael et al. (2022)	Modelling	Urban Design	Portugal
Fayyaz et al. (2022)	Literature Review	Urban Design	General	Riggs et al. (2020)	Design Guidelines and Framework	Urban Design	General
Fox (2016)	Literature Review	Urban Density	General	Shatu and Kamruzzaman (2022)	Literature Review and Framework	Urban Design	General
Fraedrich et al. (2019)	Literature review, survey and interviews	Several	Germany	Silva et al. (2021)	Survey and scenario building	Parking and Urban Design	Hungary
Gelauff et al. (2019)	Modelling	Household Relocation	Netherlands	Silva et al. (2022)	Literature Review	Urban Design	General
Golbabaei et al. (2021)	Literature Review	Several	General	Simons et al. (2018)	Literature review	Parking	USA
Gonzalez-Gonzalez et al. (2019)	Backcasting	Several	General	Soteropoulos et al. (2021)	Literature Review and Modelling	Urban Design	Austria
Guan et al. (2021)	Survey and statistical analysis	Household Relocation	China and USA	Stead and Vaddadi (2019)	Literature Review	Several	General
Gruel and Stanford (2016)	Literature Review and Forecasting	Several	General	Szimba and Hartmann (2020)	Modelling	Commute time, distance to CBD and urban sprawl	Germany
Hawkins and Nurul Habib (2018).	Literature Review	Several	General	Thakur et al. (2016)	Modelling	Household Relocation	Australia
Heinrichs (2016)	Literature Review and Forecasting	Several	General	Wang and Zhang (2021)	Modelling	Several	USA
Hiramatsu (2022)	Modelling	Household relocation	Japan	Wellik and Kockelman (2020)	Modelling	Land Use Relocation	USA
Kang et al. (2022)	Modelling	Parking	USA	Winter et al. (2021)	Modelling	Parking	Netherlands
Kang and Kim (2019)	Modelling	Diversity of Land Uses	South Korea	Xia et al. (2021)	Literature review and proposal	Urban Design	General
Kondor et al. (2019)	Modelling	Parking	Singapore	Yigitcanlar et al. (2019)	Literature Review	Several	General
Kong et al. (2018)	Modelling	Parking	General	Zakharenko (2016)	Modelling	Parking and Household Relocation	General
Kim et al. (2020)	Empirical and Modelling	Household relocation	USA	Zhang and Guhathakurta (2017)	Modelling	Parking	USA
Kumakoshi et al. (2021)	Modelling	Parking	Japan	Zhang et al. (2018)	Modelling	Road Capacity	USA
Kyung-Hwan et al. (2015)	Modelling	Road capacity and Land Use Relocation	South Korea	Zhang et al. (2015)	Modelling	Parking	USA
Larson and Zhao (2020)	Modelling	Distance to CBD and Urban Sprawl	USA	Zhang and Wang (2020)	Modelling	Parking	USA
Lee et al. (2022)	Design Guidelines	Urban Design	South Korea	Zhang and Guhathakurta (2021)	Modelling	Household Relocation	USA
Legacy et al. (2018)	Literature Review and Forecasting	Several	General	Zhou et al. (2021)	Modelling	Accessibility and Household Relocation	Singapore
Liu et al. (2022)	Modelling	Road capacity and Parking	China	Zhong et al. (2020)	Modelling	Accessibility	China

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